Unique ID: 2015014
| Division: | Coordination administrative & big data |
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| Issue Date: | February 13th 2019 |
| Last modified: | February 22nd 2019 |
Cost: FREE
Using scanner data for consumer prices
Using scanner data for consumer prices.
Using very detailed scanner data from the major supermarket chains as input for monthly consumer prices (national CPI and very soon European HICP), particularly for food and personal hygiene products.
Project Objective:
For the production of statistics
Project Outcomes:
More cost-efficient, much more detailed and equally timely food consumer price information compared to traditional method (persons recording sample prices in sample sales outlets).
Statistical Area
Project Sources
| Type Of Institution: | National statistical office |
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| Big Data Source: | Scanner data |
| Region: | Europe & Central Asia |
| Country Area: | Belgium |
| Id Country Regional: | country |
Partnerships
| Other Partners: | Other |
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| Partnership Comments: | Food retail companies (supermarket chains). |
Accessing Data
| Data Access Rights: | Only for this project |
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| Data Access Comments: | Only objective, at the moment, is calculation of consumer price indices. |
Data Coverage
| Data Coverage: | All available data |
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| Coverage Geo Pop: | Whole country / high % of market |
| Cost Implication: | Free |
| Coverage Period: | Since January 2015, start of statistical production stage. |
Project Details
| Frequency Comments: | Data come in on a weekly basis with each incoming dataset, for each of the three providers, of a size of approximately 1.5 GB (about 20 GB per month and 240 GB annually). |
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Data Quality
| Quality Framework: | Quality of output statistics |
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| Quality Aspects Evaluated: | Privacy and Security, Completeness, Usability, Time Factors, Accuracy, including selectivity |
| Validation Comments: | Data are prices for specific items, identical to previous data available (just a lot more), no conceptual or interpretation issues. |
| Quality Framework Comments: | Quality of source/input: prices from the datasets have been validated against manually collected prices, old method; turnover data have been verified for consistency over time. Quality of output statistics: simulations on scanner data have been compared with manual price collection; differences between the two have been examined in detail. |
Methodology
| Methods Used: | Traditional statistical methods |
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| Methods Comments: | Regular CPI calculations using new source. |
Technologies
| Technologies: | Relational database |
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Other
| Income Level: | High-income |
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| Iso: | BE |
| Timeframe To Produce Indicator: | NA |
| Frequency Comments: | Data come in on a weekly basis with each incoming dataset, for each of the three providers, of a size of approximately 1.5 GB (about 20 GB per month and 240 GB annually). |
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